Regardless of the type of device used to record ECG signals (e.g., ECG machines, bedside ECG monitors, Holter ECG monitors, subcutaneous ECG devices, implantable cardiac pacemakers, etc.), reliable beat detection is needed for further ECG processing and clinical diagnosis. Beat detection may involve the detection of signal peaks that exceed a preset threshold and that presumably represent heart contractions. However, the accuracy of ECG beat detection is often compromised by noise from many different sources, such as high frequency muscle noise and electromagnetic interference (EMI), low frequency noise due to respiration, saturation noise due to charge overloading of sense amplifiers, and so on. Advanced filter design can reduce, but not eliminate, these noises.
Noise in an ECG signal can cause two types of beat detection errors. A false positive (FP) occurs when a beat detection algorithm falsely generates a sense marker (i.e., an indication of a beat) when there is no QRS complex, and a false negative (FN) occurs when a beat detection algorithm fails to detect the true QRS complex. Although it is well known that large noise can cause FPs and small signal can lead to FNs, it is important to realize that FPs can also occur at low noise levels and FNs can also occur when signal amplitude is high. For example, many ECG beat detection algorithms automatically apply a blanking window after each beat detection and then dynamically adjust the sensing threshold (with an example of an auto-sensing algorithm being disclosed in U.S. Pat. No. 5,891,048). When the sensing threshold becomes too low, a sample with small noise amplitude may trigger a false detection (i.e. FP). On the other hand, each false detection may trigger a blanking window during which a true QRS may be missed from detection (i.e., an FN can result). Therefore, for reliable ECG beat detection, a high signal-to-noise ratio (SNR) is desired. In other words, when SNR is low, ECG beat detections may be unreliable.
Both FPs and FNs can cause problems for ECG rhythm analysis. For example, FPs and FNs can lead to irregular RR intervals, which could be misinterpreted by a medical device as atrial fibrillation. As another example, frequent FPs may lead to very short RR intervals, which could be misdiagnosed by the device as ventricular tachyarrhythmia. As yet another example, consecutive FNs may generate very long RR intervals, which could be misdiagnosed by the device as bradycardia or ventricular asystole. As a result, physicians may have to spend significant time and effort to examine these false rhythm classifications. Moreover, for ECG loop recorders, these false rhythm alarms could overwhelm the device's memory, which otherwise could have been used to store more useful arrhythmic episodes.
In U.S. Pat. No. 7,496,409 to Greenhut et al, the signal quality of a subcutaneous ECG is measured by several metrics, including R wave amplitude, a signal-to-noise ratio such as an R-wave peak amplitude to a maximum or average waveform amplitude between R-waves or an R-wave to T-wave amplitude ratio, a signal slope or slew rate, a low slope content, a relative high versus low frequency power, mean frequency or spectral width estimation, probability density function, normalized mean rectified amplitude, or any combination of these metrics. These metrics require high computation power and are not suitable for implementation in implantable devices, and/or have unsatisfactory performance to characterize the noise conditions.
In U.S. Pat. No. 6,230,059 to Duffin, each stored data episode is checked for noise conditions. Specifically, the most frequently occurring amplitude value (MCV) in the data set is determined, and a baseline amplitude value or range is derived from the MCV. The crossings of the baseline value are determined, and a crossing count of the total number of crossings of the baseline value is made. The crossing count is compared to a noise threshold count, and the ECG episode data is tagged as noisy if the crossing count exceeds or equals the noise threshold count, or is tagged as noise free if the noise threshold count is less than the crossing count. There are several major limitations of this method. First, it can only be applied to a data episode, and cannot be used for noise detection on a beat-by-beat basis. Second, the MCV is affected by the episode. When the episode is not representative of the quiescent ECG segments, the MCV may deviate from the true baseline. Third, the baseline range is arbitrarily defined without consideration of the signal amplitude. As a result, an ECG episode may be tagged as noisy despite its large signal-to-noise ratio (SNR), whereas another ECG episode may be tagged as noise-free despite its small SNR.
In U.S. Pat. No. 7,467,009 and U.S. Pat. No. 6,917,830 to Palreddy et al, the presence or absence of noise is determined by computing the density of local peaks or inflection points in the ECG waveform. A sample is defined as the inflection point if its amplitude is greater, or smaller, than both its preceding and the following samples by predefined threshold values. Because the predefined threshold values are not adaptive to the signal amplitude, more inflection points may be detected for an ECG signal with high SNR than another ECG signal with small SNR.
U.S. Pat. No. 7,474,247 to Hinks et al. describes a method to detect saturation noise by means of an integrator or quantizer in the front-end circuit, but does not address the more challenging task of detecting high-frequency noise introduced by muscle activities or EMI.
In U.S. Pat. No. 7,027,858 to Cao et al., the noise markers are generated if the detection intervals are considered as too short to be physiologically valid. A similar concept is also described in U.S. Pat. Appl. 2008/0161873 to Gunderson, and in U.S. Pat. Appl. No. 2008/0300497 to Krause et al. These interval-based noise detection methods are neither sensitive nor specific for noise detection.
U.S. Pat. No. 7,570,989 to Baura et al. describes a method which uses the change in RR interval from the previous beat, and the change in mean squared error (MSE) from the last n (e.g. n=2) good beats, to determine whether the beat under analysis should be further processed. The MSE change and the RR interval change could be caused by many factors other than noise, so this method is not reliable for noise detection.
U.S. Pat. No. 5,999,845 to dePinto describes a method to detect wideband noise by measuring the running average of the squared sample values of the ECG signal with QRS complex blanked.
U.S. Pat. No. 5,647,379 to Meltzer describes a method to detect the level of EMI component of an input biomedical signal by calculating a correlation function. However, none of these methods has proven accuracy in quantifying the ECG signal quality.
US Pat. Appl. Pub. 2010/0312131 A1 to Naware et al. describes a method for noise detection by counting the number of noise threshold crossings in a noise detection window, where the noise threshold is preferably a specified percentage of the beat detection sensing threshold. This method has several shortcomings. First, the beat detection sensing threshold may or may not be adaptive to the signal amplitude. As a result, there is no guarantee that the noise threshold reflects the signal amplitude, and thus the method does not take into account the signal to noise ratio in noise detection. Second, the noise window is either fixed or back-to-back, and thus the method does not allow sample-by-sample or real-time noise detection. Third, the noise detection window is triggered by sensed ventricular events. Therefore, under-sensing of ventricular events will not trigger noise detection, thereby leading to under-detection of noises. On the other hand, over-sensing of large artefacts may lead to too high of a noise threshold, which can also lead to under-detection of noises.